Design of intelligent traffic light system based on genetic algorithm to optimize Elman neural network

Kaikai Xie; Shaoming Li; Zhibo Sun

doi:10.1117/12.3024199

14 February 2024 Design of intelligent traffic light system based on genetic algorithm to optimize Elman neural network

Kaikai Xie, Shaoming Li, Zhibo Sun

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130181D (2024) https://doi.org/10.1117/12.3024199
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

In view of the current traffic congestion problem, if only rely on the traditional traffic light indication is no longer able to deal with road traffic congestion, so we need to adjust the traffic light indication time in real time according to the realtime traffic conditions on the road, so as to achieve the effective unblocking of intelligent traffic lights. This paper presents the design of intelligent traffic light system based on genetic algorithm to optimize Elman neural network. First of all, this design will take STM32F103 single chip microcomputer as the core to build the hardware circuit, including the collection of traffic flow, automatic control and feedback of the closed-loop control system. In terms of software design, this design builds a system program and processes the collected traffic flow information by optimizing Elman neural network algorithm based on genetic algorithm. According to the obtained results, the display time of traffic lights in the next cycle is changed, so as to achieve the purpose of regulating traffic flow and improving traffic.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaikai Xie, Shaoming Li, and Zhibo Sun "Design of intelligent traffic light system based on genetic algorithm to optimize Elman neural network", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130181D (14 February 2024); https://doi.org/10.1117/12.3024199

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